Improving Multiple Time Series Forecasting with Data Stream Mining Algorithms

This paper proposes a hybrid ensemble learning approach that combines statistical and data stream mining algorithms to obtain better forecasting performance in multiple time series prediction problems. Although some multiple time series algorithms perform surprisingly well in a variety of domains, i...

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Bibliographic Details

Published in:	Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics pp. 1060 - 1067
Main Authors:	Mochinski, Marcos Alberto, Paul Barddal, Jean, Enembreck, Fabricio
Format:	Conference Proceeding
Language:	English
Published:	IEEE 11.10.2020
Subjects:	Data mining data stream mining algorithms Forecasting hybrid ensemble multiple time series Prediction algorithms Real-time systems Social networking (online) Time series analysis Time series forecasting Training
ISSN:	2577-1655
Online Access:	Get full text
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